activity
20222026
most citedQuantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment

21 citations · 21 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

3 papers · 1 filter

cs.LG2026

QFlash: Bridging Quantization and Memory Efficiency in Vision Transformer Attention

Sehyeon Oh, Yongin Kwon, Jemin Lee

FlashAttention improves efficiency through tiling, but its online softmax still relies on floating-point arithmetic for numerical stability, making full quantization difficult. We…

cs.LG2024

MLTuner: Efficient Code Tuning via Multi-Level Machine Learning Models

JooHyoung Cha, Munyoung Lee, Jinse Kwon +3

The increasing complexity of deep learning models necessitates specialized hardware and software optimizations, particularly for deep learning accelerators. Existing autotuning met…

cs.LG2022★ 21 cited

Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment

Jemin Lee, Misun Yu, Yongin Kwon +1

To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it is necessary to compress the CNN models by performing quantization, whereby precision r…